def plot(cls_num):
os.chdir(os.path.join(wltpdb.mydir, wltpdb.samples_dir))
cls_name = class_name_from_num(cls_num)
data = data_meanN_gears(cls_num)
cols = data.keys()
assert len(cols) == 4, cols
parts = zip(model.get_class_part_names(), [
dict(data_fmt='1', data_kws=dict(color='#0000ff', markersize=4.8, ),
diff_kws=dict(linestyle=':', linewidth=4)
),
dict(data_fmt='_', data_kws=dict(color='#0033cc', markersize=4.8, fillstyle='none'),
diff_kws=dict(linestyle='-.',linewidth=4)
),
dict(data_fmt='o', data_kws=dict(color='#006699', markersize=4.8, fillstyle='none'),
diff_kws=dict(linestyle='-',linewidth=4)
),
dict(data_fmt='+', data_kws=dict(color='#009966', markersize=4.8, fillstyle='none'),
diff_kws=dict(linestyle='--', linewidth=4)
),
])
axes_tuple = None
for (part, kws) in parts:
try:
## veh part N/gear/Py/Heinz
X = data.loc[(slice(None), part), cols[0]]
Y = data.loc[(slice(None), part), cols[1]]
X_REF = data.loc[(slice(None), part), cols[2]]
Y_REF = data.loc[(slice(None), part), cols[3]]
except KeyError:
continue
axes_tuple, artist_tuple = plots.plot_xy_diffs_arrows(
X, Y, X_REF, Y_REF,
data_label='Python, %s'%part, diff_label='Diffs %s'%part,
diff_cmap=cm.cool, #@UndefinedVariable
title="%s: Python(arrow-heads) compared to AccessDb-2secAccel (arrow-tails)" % cls_name,
x_label=r'Mean Gear',
y_label='$Mean EngineSpeed [rpm]$',
axes_tuple=axes_tuple,
**kws
)
axes_tuple[0].legend(loc=1, fancybox=True, framealpha=0.5)
axes_tuple[1].legend(loc=2, fancybox=True, framealpha=0.5)
return data, axes_tuple
# Licensed under the EUPL (the 'Licence');
# You may not use this work except in compliance with the Licence.
# You may obtain a copy of the Licence at: http://ec.europa.eu/idabc/eupl
import os
import sys
from wltp import (plots, model)
from wltp.test import wltp_db_tests as wltpdb
from matplotlib import pyplot as plt, cm
import pandas as pd
classes = ['class1', 'class2', 'class3b']
part_names = model.get_class_part_names()
def class_name_from_num(cls_num):
return classes[cls_num-1]
def data_meanN_gears(cls_num):
"""
Constructs meanN vs gears, hierarchically grouped by: vehicle, python/heinz, class_part
:return: df like that::
python heinz
gear n gear n
1154 Low 2.020374 1078.312480 2.039049 1105.891341
Medium 3.577367 1364.552656 3.618938 1353.300231
High 4.028571 1694.902241 4.160440 1618.457143
ExtraHigh 4.266254 2532.802858 4.424149 2514.894737
